Identifying the four genes [amyloid precursor protein, presenilin I and 2 and apolipoprotein El already associated with Alzheimer disease (AD) has opened new avenues of research and greatly enhanced our understanding of this common and complex disease. Still our understanding of the genetic basis of AD is far from complete. Nearly half of the genetic effect has not been explained. Our recently completed Collaborative Alzheimer Project (CAP) genomic screen, in the largest set of AID families to date (455 families, 726 sibpairs), identified a novel gene localization to 9p21 (MLOD = 3.43; MLS = 3.31 in the overall dataset and MLOD = 3.94; MLS = 4.41 in the subset of autopsy confirmed cases). Preliminary follow-up analyses have demonstrated an allelic association with several single nucleotide polymorphisms in this region (P<0.02), further strengthening our evidence for an AD gene on 9p. A similar localization has been confirmed in an independent study of an isolated Arab population. The present application is a focused effort to integrate statistical and molecular genomic approaches to identify the 9p gene. Breathtaking advances in human genetics, including the completion of the draft human genome sequence and the identification and mapping of millions of single-nucleotide polymorphisms (SNPs), give us the necessary tools to accomplish this goal. We will integrate statistical and molecular methods to identify and test candidate genes on 9p and isolate the gene that modulates the risk of AD. This will be done using complementary datasets of multiplex families, discordant sibpairs, and case-control pairs. We will initially genotype five anchor microsatellite markers in all newly obtained families (-250). Second, using a combination of in silico and laboratory discovery, we will identify SNPs within genes and determine the minimal set of SNPs encoding the haplotypes for each candidate gene. Third, we will perform high throughput genotyping on the minimal SNP set for each candidate gene through all three datasets. We will analyze these data using statistical methods appropriate for each dataset to look for association between a candidate gene and AD. Finally, we will carry out detailed mutation and expression analysis to fully characterize the most likely candidate genes and isolate the 9p AD gene.